▮ Learning Online While I was still learning about AI to become a data scientist as a residential architect, there was a lot of high-quality and free content on Youtube that helped me along the way. So for this post,…
▮ Data Shift After deploying your model, you’ll need to keep on maintaining them because data is constantly changing. So for this post, I’d like to share three different types of data distribution shifts that may occur which can degrade…
▮ Leakage Data leakage refers to the phenomenon when a form of a label “leaks” into the set of features used for making predictions even though this information is not available during inference in production. Data leakage is hard to…
▮ Low GPU Utilization Ideally, we want to use our GPU machines during training/inference fully. However, if you are not considering GPU utilization when creating training scripts for your deep learning model, the odds are that the GPU utilization rate…
▮ Stack Structure For this post, I’d like to share an intuitive overview of the basic steps of how your code communicates with the GPU when implementing deep learning models. Starting from the GPU side, the general process takes the…
▮ Sharing your findings In many cases, at the final phase of your data science project, you will need to organize and represent your findings to your audiences through a report as a deliverable. These reports can be in many…
▮ Elements Metric learning aims to measure the similarity between samples while using distance metrics for learning. Due to a survey in 2019, this field seems to become more and more important. So for this post, I’d like to share…
▮ Image Classification When doing image classification tasks, there are mainly 2 approaches; Traditional classification and Metric Learning. Here are the differences between the two approaches. ▮ Traditional Classification The traditional classification approach classifies images by outputting the probability for…
▮ Keeping Track When developing an ML model, there is a lot of information to keep track of. Data scientists have to constantly experiment and the information can get easily chaotic. Ideally, all the information should be organized so that…
▮ Execution Methods Execution methods can become confusing to people who have just started machine learning. So for this post, I’d like to share the difference between graph execution and eager execution. ▮ Graph Execution Fig.1 – Graph Execution Graph…